class_docstr: Tracking Visualization Hook. Used to visualize validation and testing
  process prediction results.
  
  In the testing phase:
  
  1. If ``show`` is True, it means that only the prediction results are
      visualized without storing data, so ``vis_backends`` needs to
      be excluded.
  2. If ``test_out_dir`` is specified, it means that the prediction results
      need to be saved to ``test_out_dir``. In order to avoid vis_backends
      also storing data, so ``vis_backends`` needs to be excluded.
  3. ``vis_backends`` takes effect if the user does not specify ``show``
      and `test_out_dir``. You can set ``vis_backends`` to WandbVisBackend or
      TensorboardVisBackend to store the prediction result in Wandb or
      Tensorboard.
  
  Args:
      draw (bool): whether to draw prediction results. If it is False,
          it means that no drawing will be done. Defaults to False.
      frame_interval (int): The interval of visualization. Defaults to 30.
      score_thr (float): The threshold to visualize the bboxes
          and masks. Defaults to 0.3.
      show (bool): Whether to display the drawn image. Default to False.
      wait_time (float): The interval of show (s). Defaults to 0.
      test_out_dir (str, optional): directory where painted images
          will be saved in testing process.
      backend_args (dict): Arguments to instantiate a file client.
          Defaults to ``None``.
docstr: Args:
      img_data_sample (DetDataSample): single image output.
      step (int): The index of the current image.
docstr_tok: ["Args",":","img_data_sample","(","DetDataSample",")",":","single","image","output",".","step","(","int",")",":","The","index","of","the","current","image","."]
code: def visualize_single_image(
      self, img_data_sample: DetDataSample, step: int
  ) -> None:
      """
      Args:
          img_data_sample (DetDataSample): single image output.
          step (int): The index of the current image.
      """
      img_path = img_data_sample.img_path
      img_bytes = get(img_path, backend_args=self.backend_args)
      img = mmcv.imfrombytes(img_bytes, channel_order="rgb")
  
      out_file = None
      if self.test_out_dir is not None:
          video_name = img_path.split("/")[-3]
          mkdir_or_exist(osp.join(self.test_out_dir, video_name))
          out_file = osp.join(
              self.test_out_dir, video_name, osp.basename(img_path)
          )
  
      self._visualizer.add_datasample(
          osp.basename(img_path) if self.show else "test_img",
          img,
          data_sample=img_data_sample,
          show=self.show,
          wait_time=self.wait_time,
          pred_score_thr=self.score_thr,
          out_file=out_file,
          step=step,
      )
  
imports: import os.path
  import warnings
  from typing import Optional, Sequence
  import mmcv
  import numpy
  from mmengine.fileio import get
  from mmengine.hooks import Hook
  from mmengine.runner import Runner
  from mmengine.utils import mkdir_or_exist
  from mmengine.visualization import Visualizer
  from mmdet.datasets.samplers import TrackImgSampler
  from mmdet.registry import HOOKS
  from mmdet.structures import DetDataSample, TrackDataSample
  from mmdet.structures.bbox import BaseBoxes
  from mmdet.visualization.palette import _get_adaptive_scales
label_desc: Use image node_id 4 for calling the TrackVisualizationHook obj's underlying member method code with example usage: obj.visualize_single_image(img_data_sample, step) without return types